The marine diesel engine exhaust gas temperature baseline model based on particle swarm optimised generalised regression neural network

ABSTRACT

This study addresses limitations in traditional condition monitoring for marine diesel engine (MDE) reliability and stability by proposing a hybrid machine learning and deep learning (DL) model called Particle Swarm Optimization-Generalized Regression Neural Network (PSO-GRNN) for real-time exhaust gas temperature (EGT) monitoring. The model integrates Mahalanobis distance (MD) calibration, wavelet packet denoising, and Pearson correlation analysis for comprehensive data preprocessing. Validation is conducted using historical '6L34DF' diesel engine data. Particle Swarm Optimization (PSO) optimizes the spread value (σ) of the Generalized Regression Neural Network (GRNN) to establish an optimal baseline model for EGT prediction. Experimental results show that PSO-GRNN outperforms Backpropagation Neural Network (BPNN), GRNN, Bidirectional Gated Recurrent Unit (BiGRU), Genetic Algorithm- Generalized Regression Neural Network (GA-GRNN), and Deep Belief Network-Support Vector Regression (DBN-SVR) in terms of training time and accuracy, demonstrating its suitability for MDE baseline modeling.

KEYWORDS:

• MDE; EGT; PSO; GRNN; baseline model

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Due to the nature of the research, due to commercial supporting data is not available.

Author contributions

Experimental Design and Conception: JS HZ. Experimental verification: JS ZW. Experimental data analysis: JS KJ CC. Algorithm Design and Experimental Methods: JS HZ CC. Wrote the paper: JS Write the thesis: JS. The revision of the first draft of the paper: HZ CC.

Additional information

Funding

This research is funded by the National Engineering Research Center of Ship Transportation Control System, the research project of digital model construction technology of typical ship equipment, [grant number W23CG000124].We would like thank the Liaoning Provincial Department of Natural Resources for funding the project of Development of Ship Operation Condition Monitoring and Simulation Platform, Project No. 1638882993269. Thanks to the Liaoning Provincial Department of Science and Technology for funding the project of Research and Application of Smart Ship Digital Twin Information Platform, Project No. 2022H1/10800097.

Notes on contributors

Hong Zeng

Hong Zeng, male, received Ph.D. degree in Marine Engineering from Dalian Maritime University, in 2012. Since 2013, he has been working as an Associate Professor with Marine Engineering College, Dalian Maritime University, China. From 2018 to 2019, he was a Visiting Researcher with the Department of Naval Architecture, Ocean and Marine Engineering at the University of Strathclyde, UK. He has published more than 30 journal and conference papers. His research in-terests include the application of the new generation of information technology in marine engi-neering, mainly focus on the modelling, simulation and control in marine engineering.

Jianping Sun

Jianping Sun graduated from Dalian Maritime University in 2021 with a major in Marine Engineering. And he is currently pursuing his master's degree in Marine Engineering at Dalian Maritime University. His research topics are Research on baseline model and trend prediction model of Marine diesel engine thermal parameters.

Cai Chen

Cai Chen graduated from Dalian Maritime University in 2021 with a major in Marine Engineering. And he is currently pursuing his master's degree in Marine Engineering at Dalian Maritime University. His research topics are Research on fault diagnosis of Marine diesel engines.

Kuo Jiang

Kuo Jiang, male, received his Bachelor's Degree in Marine Engineering from Ningbo University in 2021. He is currently pursuing his master's degree in Marine Engineering at Dalian Maritime University, China. His research interests focus on dual-fuel engine simulation and optimization.

Zefan Wu

Zefan Wu graduated from Dalian Maritime University in 2021 with a major in Marine Engineering. And he is currently pursuing his master's degree in Marine Engineering at Dalian Maritime University. His research topics are research on marine dual-fuel engine modeling and waste bypass valves.